Summary of Preserving Multi-modal Capabilities Of Pre-trained Vlms For Improving Vision-linguistic Compositionality, by Youngtaek Oh et al.
Preserving Multi-Modal Capabilities of Pre-trained VLMs for Improving Vision-Linguistic Compositionality
by Youngtaek Oh, Jae Won Cho, Dong-Jin Kim, In So Kweon, Junmo Kim
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, Fine-grained Selective Calibrated CLIP (FSC-CLIP), aims to enhance compositional understanding in pre-trained vision and language models (VLMs) without sacrificing performance in zero-shot multi-modal tasks. Traditional fine-tuning approaches often trade off between compositionality and multi-modality, as global hard negative loss can degrade the model’s representational integrity. FSC-CLIP integrates local hard negative loss and selective calibrated regularization to provide fine-grained negative supervision while preserving the model’s representational integrity. The method is evaluated across diverse benchmarks for both compositionality and multi-modal tasks, demonstrating that FSC-CLIP achieves state-of-the-art performance in both areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FSC-CLIP is a new way to help computers understand language and pictures together better. Right now, some computer models are really good at one or the other, but not both. The problem is with how we train these models. We need to find a way that makes them good at both understanding words and images without losing what they already know. FSC-CLIP does just that. It uses a special kind of training that helps the model learn more about language and pictures together, while still being good at one or the other. The result is a computer model that can understand language and pictures much better. |
Keywords
» Artificial intelligence » Fine tuning » Multi modal » Regularization » Zero shot
